When a human speaks a word, they cause their voice to make a time-varying pattern of sounds. These sounds are waves of pressure that propagate through the air. The sounds are captured by a sensor, such as a microphone or microphone array, and turned into a sequence of numbers representing the pressure change over time. The automatic speech recognition system converts this time-pressure signal into a time-frequency-energy signal. It has been trained on a curated set of labeled speech sounds, and labels the sounds it is presented with. These acoustic labels are combined with a model of word pronunciation and a model of word sequences, to create a textual representation of what was said.

Instead of exploring one part of this process deeply, this course is designed to give an overview of the components of a modern ASR system. In each lecture, we describe a component's purpose and general structure. In each lab, the student creates a functioning block of the system. At the end of the course, we will have built a speech recognition system almost entirely out of Python code.

What you'll learn

Fundamentals of Speech Recognition

Basic Signal Processing for Speech Recogntion

Acoustic Modeling and Labeling

Common Algorithms for Language Modeling

Decoding Acoustic Features into Speech

Prerequisites

Some python experience

Basic Machine Learning principles

Knowledge of probability and statistics

Meet the instructors

Adrian Leven

Content Developer
Microsoft Corporation

Adrian Leven is a Content Developer at Microsoft Learning with a focus on Human-Computer Interaction. He received his B.S. In Computer Science from Stanford University.